Day-to-day evolution of the traffic network with Advanced Traveler Information System

Since the notion of user equilibrium (UE) was proposed by Wardrop [13], it has become a cornerstone for traffic assignment analysis. But, it is not sufficient to only ask whether equilibrium exists or not; it is equally important to ask whether and how the system can achieve equilibrium. Meanwhile, stability is an important performance in the sense that if equilibrium is unsustainable, both the equilibrium and the trajectory are sensitive to disturbances, even a small perturbation will result in the system evolution away from the equilibrium point. These incentive a growing interest in day-to-day dynamics. In this paper, we develop a dynamical system with Advanced Traveler Information System (ATIS) and study the stability of the network with ATIS. A simple network is used to simulate the model, and the results show that there exist periodic attractors in the traffic network in some cases (for example, the market penetration level of ATIS is 0.25 and traffic demand is 2 unit). It is found that the logit parameter of the dynamical model and the traffic demand can also affect the stability of the traffic network. More periodic attractors appear in the system when the traffic demand is large and the low logit parameter can delay the appearance of periodic attractors. By simulation, it can be concluded that if the range of the periodic attractors’ domain of the simple network is known, the road pricing based on the range of the attraction domain is effective to alleviate the instability of the system.     

Deriving Behavior of Boolean Bioregulatory Networks from Subnetwork Dynamics

In the well-known discrete modeling framework developed by R. Thomas, the structure of a biological regulatory network is captured in an interaction graph, which, together with a set of Boolean parameters, gives rise to a state transition graph describing all possible dynamical behaviors. For complex networks the analysis of the dynamics becomes more and more difficult, and efficient methods to carry out the analysis are needed. In this paper, we focus on identifying subnetworks of the system that govern the behavior of the system as a whole. We present methods to derive trajectories and attractors of the network from the dynamics suitable subnetworks display in isolation. In addition, we use these ideas to link the existence of certain structural motifs, namely circuits, in the interaction graph to the character and number of attractors in the state transition graph, generalizing and refining results presented in [10]. Lastly, we show for a specific class of networks that all possible asymptotic behaviors of networks in that class can be derived from the dynamics of easily identifiable subnetworks.      

Exclusion sensitivity of Boolean functions

Recently the study of noise sensitivity and noise stability of Boolean functions has received considerable attention. The purpose of this paper is to extend these notions in a natural way to a different class of perturbations, namely those arising from running the symmetric exclusion process for a short amount of time. In this study, the case of monotone Boolean functions will turn out to be of particular interest. We show that for this class of functions, ordinary noise sensitivity and noise sensitivity with respect to the complete graph exclusion process are equivalent. We also show this equivalence with respect to stability. After obtaining these fairly general results, we study “exclusion sensitivity” of critical percolation in more detail with respect to medium-range dynamics. The exclusion dynamics, due to its conservative nature, is in some sense more physical than the classical i.i.d. dynamics. Interestingly, we will see that in order to obtain a precise understanding of the exclusion sensitivity of percolation, we will need to describe how typical spectral sets of percolation diffuse under the underlying exclusion process.     

Dynamics of Coupled Cell Networks: Synchrony, Heteroclinic Cycles and Inflation

We consider the dynamics of small networks of coupled cells. We usually assume asymmetric inputs and no global or local symmetries in the network and consider equivalence of networks in this setting; that is, when two networks with different architectures give rise to the same set of possible dynamics. Focussing on transitive (strongly connected) networks that have only one type of cell (identical cell networks) we address three questions relating the network structure to dynamics. The first question is how the structure of the network may force the existence of invariant subspaces (synchrony subspaces). The second question is how these invariant subspaces can support robust heteroclinic attractors. Finally, we investigate how the dynamics of coupled cell networks with different structures and numbers of cells can be related; in particular we consider the sets of possible “inflations” of a coupled cell network that are obtained by replacing one cell by many of the same type, in such a way that the original network dynamics is still present within a synchrony subspace. We illustrate the results with a number of examples of networks of up to six cells.     

Synchronism in population networks with non linear coupling

A quite general spatially explicit metapopulation model featuring density-dependent dispersal is proposed. A study of the stability of synchronized attractors is done based on an explicit calculation of the transverse Lyapunov number of these attractors. An analytic expression for the transverse Lyapunov number depending on the Lyapunov number of the synchronous trajectory, the eigenvalues of the network configuration matrix and the function modelling the density-dependent dispersal (the number of migrants as a function of the local density) is presented. Numerical simulations are also performed upon selecting biologically relevant features, such as local dynamics, network topology and density-dependent dispersal mechanism.     

Low dimensional homeochaos in coevolving host–parasitoid dimorphic populations: Extinction thresholds under local noise

A discrete time model describing the population dynamics of coevolution between host and parasitoid haploid populations with a dimorphic matching allele coupling is investigated under both determinism and stochastic population disturbances. The role of the properties of the attractors governing the survival of both populations is analyzed considering equal mutation rates and focusing on host and parasitoid growth rates involving chaos. The purely deterministic model reveals a wide range of ordered and chaotic Red Queen dynamics causing cyclic and aperiodic fluctuations of haplotypes within each species. A Ruelle–Takens–Newhouse route to chaos is identified by increasing both host and parasitoid growth rates. From the bifurcation diagram structure and from numerical stability analysis, two different types of chaotic sets are roughly differentiated according to their size in phase space and to their largest Lyapunov exponent: the Confined and Expanded attractors. Under the presence of local population noise, these two types of attractors have a crucial role in the survival of both coevolving populations. The chaotic confined attractors, which have a low largest positive Lyapunov exponent, are shown to involve a very low extinction probability under the influence of local population noise. On the contrary, the expanded chaotic sets (with a higher largest positive Lyapunov exponent) involve higher host and parasitoid extinction probabilities under the presence of noise. The asynchronies between haplotypes in the chaotic regime combined with low dimensional homeochaos tied to the confined attractors is suggested to reinforce the long-term persistence of these coevolving populations under the influence of stochastic disturbances. These ideas are also discussed in the framework of spatially-distributed host–parasitoid populations.     

Evidence of low-dimensional dynamics in a traffic noise time series

The noise level measurements were made at 10 s interval near a busy road which admits one-way traffic only. The time series thus obtained was analysed using non-linear dynamical techniques. The results of the analysis suggest that the underlying dynamical process could be deterministic. It appears that the data support periodic and quasiperiodic attractors. The presence of colour fluctuations in the time series could be attributed to shuttling of the dynamics between these two attractors.     

Inferring Boolean functions via higher-order correlations

Both the Walsh transform and a modified Pearson correlation coefficient can be used to infer the structure of a Boolean network from time series data. Unlike the correlation coefficient, the Walsh transform is also able to represent higher-order correlations. These correlations of several combined input variables with one output variable give additional information about the dependency between variables, but are also more sensitive to noise. Furthermore computational complexity increases exponentially with the order. We first show that the Walsh transform of order 1 and the modified Pearson correlation coefficient are equivalent for the reconstruction of Boolean functions. Secondly, we also investigate under which conditions (noise, number of samples, function classes) higher-order correlations can contribute to an improvement of the reconstruction process. We present the merits, as well as the limitations, of higher-order correlations for the inference of Boolean networks.     

Chaotic attractor generation and critical value analysis via switching approach

The generation of novel chaotic funnel-shaped attractors is introduced and the analysis of related critical values is given with a proposed switching method in this paper. The underlying mechanism involves a simple three-dimensional switched system and a hysteretically switching signal. Moreover, theoretic analysis is carried out to study the attractor generation and the corresponding critical values by fully utilizing the specific structure of the non-smooth system. Based on carefully derivation, the critical values and related stability regions of the created attractors are estimated explicitly, which is usually impossible for general non-smooth dynamics. In addition, it is demonstrated by simulation that various attractor patterns are generated conveniently by adjusting suitable system parameters.     

Random dynamics and limiting behaviors for 3D globally modified Navier–Stokes equations driven by colored noise

This paper is mainly concerned with the long-term random dynamics for the nonautonomous 3D globally modified Navier–Stokes equations with nonlinear colored noise. We first prove the existence of random attractors of the nonautonomous random dynamical system generated by the solution operators of such equations. Then we establish the existence of invariant measures supported on the random attractors of the underlying system. Random Liouville-type theorem is also derived for such invariant measures. Moreover, we further investigate the limiting relationship of invariant measures between the above equations and the corresponding limiting equations when the noise intensity approaches to zero. In addition, we show the invariant measures of such equations with additive white noise can be approximated by those of the corresponding equations with additive colored noise as the correlation time of the colored noise goes to zero.     

A novel method of control using chaotic dynamics in systems having many degrees-of-freedom

Chaotic dynamics in systems having many degrees of freedom are investigated from the viewpoint of harnessing chaos and is applied to complex control problems to indicate that chaotic dynamics has potential capabilities for complex control functions by simple rule(s). An important idea is that chaotic dynamics generated in these systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded designed attractors in high-dimensional state space. A key point is that, with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the set context is one of typical ill-posed problems, is solved with the use of chaos in a recurrent neural network model. Our computer experiments show that the success rate over several hundreds trials is much better, at least, than that of a random number generator. Our functional simulations indicate that harnessing of chaos is one of essential ideas to approach mechanisms of brain functions. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Dynamics of the Stochastic Chemostat Model with Monod-Haldane Response Function

This paper is devoted to the asymptotic dynamics of stochastic chemostat model with Monod-Haldane response function. We first prove the existence of random attractors by means of the conjugacy method and further construct a general condition for internal structure of the random attractor, implying extinction of the species even with small noise. Moreover, we show that the attractors of Wong-Zakai approximations converges to the attractor of the stochastic chemostat model in an appropriate sense.     

Maximal Unstable Dissipative Interval to Preserve Multi-scroll Attractors via Multi-saturated Functions

In this paper, we present families of piecewise linear systems which are controlled by a continuous piecewise monoparametric control function for the generation of monoparametric families of multi-scroll attractors. Thus, the maximum range of values that the parameter set can take in order to preserve the useful dynamics for generating of multi-scroll attractors is found and it will be called maximal robust dynamics interval. This class of dynamical systems is the result of combining two or more unstable “one-spiral” trajectories. We give necessary and sufficient conditions in order to preserve multi-scroll attractors in terms of a parameter, i.e., a family of multi-scroll attractors is generated by means of a family of switching systems with multiple monoparametric companion matrices. Lastly, we provide an example to show how the developed theory works.     

Generalizations of the concept of marginal synchronization of chaos

Generalizations of the concept of marginal synchronization between chaotic systems, i.e. synchronization with zero largest conditional Lyapunov exponent, are considered. Generalized marginal synchronization in drive–response systems is defined, for which the function between points of attractors of different systems is given up to a constant. Auxiliary system approach is shown to be able to detect this synchronization. Marginal synchronization in mutually coupled systems which can be viewed as drive–response systems with the response system influencing the drive system dynamics is also considered, and an example from solid-state physics is analyzed. Stability of these kinds of synchronization against changes of system parameters and noise is investigated. In drive–response systems generalized marginal synchronization is shown to be rather sensitive to the changes of parameters and may disappear either due to the loss of stability of the response system, or as a result of the blowout bifurcation. Nonlinear coupling of the drive system to the response system can stabilize marginal synchronization.     

On three types of dynamics and the notion of attractor

We propose a theoretical framework for explaining the numerically discovered phenomenon of the attractor–repeller merger. We identify regimes observed in dynamical systems with attractors as defined in a paper by Ruelle and show that these attractors can be of three different types. The first two types correspond to the well-known types of chaotic behavior, conservative and dissipative, while the attractors of the third type, reversible cores, provide a new type of chaos, the so-called mixed dynamics, characterized by the inseparability of dissipative and conservative regimes. We prove that every elliptic orbit of a generic non-conservative time-reversible system is a reversible core. We also prove that a generic reversible system with an elliptic orbit is universal; i.e., it displays dynamics of maximum possible richness and complexity.     

Random Point Attractors Versus Random Set Attractors

The notion of an attractor for a random dynamical system withrespect to a general collection of deterministic sets is introduced.This comprises, in particular, global point attractors and globalset attractors. After deriving a necessary and sufficient conditionfor existence of the corresponding attractors it is proved thata global set attractor always contains all unstable sets ofall of its subsets. Then it is shown that in general randompoint attractors, in contrast to deterministic point attractors,do not support all invariant measures of the system. However,for white noise systems it holds that the minimal point attractorsupports all invariant Markov measures of the system.     

Effect of bounded noise on chaotic motions of stochastically perturbed slowly varying oscillator

The effect of bounded noise on the chaotic behavior of a class of slowly varying oscillators is investigated. The stochastic Melnikov method is employed and then the criteria in both mean and mean-square sense are derived. The threshold amplitude of bounded noise given by stochastic Melnikov process is in good comparison with one determined by the numerical simulation of top Lyapunov exponents. The presence of noise scatters the chaotic domain in parameter space and the larger noise intensity results in a sparser and more irregular region. Both the simple cell mapping method and the generalized cell mapping method are applied to demonstrate the effects of noises on the attractors. Results show that the attractors are diffused and smeared by bounded noise and if the noise intensity increases, the diffusion is exacerbated.     

On the computation of fixed points in Boolean networks

In this paper we study the problem of finding fixed points of AND-NOT Boolean networks and relate it to the problem of finding maximal independent sets of a graph. Furthermore, we provide a sharp upper bound for the number of fixed points of normal AND-NOT Boolean networks. We also show that any AND-NOT Boolean network can be transformed into a normal AND-NOT Boolean network.